Name
Abstract | ||
|---|---|---|
Numerous causal structure discovery methods have been proposed recently but none of them has taken possible time-varying structure into consideration. In this paper, we introduce a notion of causal time-varying dynamic Bayesian network (CTV-DBN) and define a causal boundary to govern cross time information sharing. Although spatio-temporal data have been investigated by multiple disciplines; by reducing structure discovery into a set of optimization problems, CTV-DBN is a scalable solution targeting large datasets. CTV-DBN is constructed using asymmetric kernels to address sample scarcity and to adhere to causal principles; while maintaining good variance and bias trade-off. We explore trajectory data collected from mobile devices which are known to exhibit heterogeneous patterns, data sparseness and distribution skewness. Contrary to a naive method to divide space by grids, we capture the moving objects' view of space by using density-based clustering to overcome the problems. In our experiments, CTV-DBN is used to reveal the evolution of time-varying region macro structure in a ring road system based on trajectories, and to obtain a local time-varying road junction dependency structure based on static traffic flow sensor data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-05810-8_16 | DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2014, PT I |
Field | DocType | Volume |
Data mining,Causal structure,Skewness,Computer science,Temporal database,Cluster analysis,Macro,Optimization problem,Dynamic Bayesian network,Scalability | Conference | 8421 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.50 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victor W. Chu | 1 | 83 | 10.18 |
| Raymond K. Wong | 2 | 661 | 105.45 |
| Wei Liu | 3 | 468 | 37.36 |
| Fang Chen | 4 | 156 | 49.84 |